Presented During:
Tuesday, June 25, 2024: 12:00 PM - 1:15 PM
COEX
Room:
Grand Ballroom 103
Poster No:
2224
Submission Type:
Abstract Submission
Authors:
Peer Herholz1, Daniel Levitas2, James Kent3, Arianna Sala4, Cyrus Eierud5, Brad Caron3, Kimberly Ray6, Anibal Solon Heinsfeld3, Hamsanandini Radhakrishnan7, Sydney Covitz8, Kahini Mehta7, Julio Peraza9, Angela Laird10, Alejandro De La Vega11, Caterina Gratton12, Melanie Ganz13, Dora Hermes14, Sina Mansour L.15, Christophe Phillips16, Christopher Markiewicz17, Ross Blair18, Paul Wighton19, Granville Matheson20, Mathieu Guay-Paquet21, Dustin Moraczewski22, Robert Oostenveld23, Arnaud Delorme24, Cyril Pernet25, Robert Smith26, Eugene Duff27, Oscar Esteban28, Vince Calhoun29, Russell Poldrack30, Theodore Satterthwaite31, Ariel Rokem32, Franco Pestilli2
Institutions:
1The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Canada, 2University of Texas, Austin, Austin, TX, 3Department of Psychology, University of Texas at Austin, Austin, TX, 4Université De Liège, Liege, Sart-Tilman, 5Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, 6UT Austin, Austin, TX, 7University of Pennsylvania, Philadelphia, PA, 8Stanford, Palo Alto, CA, 9Florida International University, Miami, FL, 10Department of Physics, Florida International University, Miami, FL, 11University of Texas at AUstin, Austin, TX, 12Florida State University, Tallahassee, FL, 13University of Copenhagen, Copenhagen, Denmark, 14Mayo Clinic, Rochester, MN, 15National University of Singapore, Singapore, NA, 16University of Liège, Liège, Belgium, 17Stanford University, Stanford, CA, 18Stanford University, San Francisco, CA, 19Martinos Center for Biomedical Imaging at MGH, Boston, MA, 20Columbia University / Karolinska Institutet, Solna, Stockholms län, 21NeuroPoly Lab, Polytechnique Montreal, Montreal, Canada, 22Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, 23Donders Centre For Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands, 24SCCN, INC, University of California San Diego, La Jolla, CA, 25Neurobiology Research Unit, Copenhagen, Denmark, 26Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, 27Imperial College London, London, England, 28Lausanne University Hospital and University of Lausanne, Lausanne, VD, 29GSU/GATech/Emory, Decatur, GA, 30Stanford University, Palo Alto, CA, 31UPenn, Philadelphia, PA, 32University of Washington, Seattle, WA
First Author:
Peer Herholz
The Neuro (Montreal Neurological Institute-Hospital), McGill University
Montreal, Canada
Co-Author(s):
James Kent
Department of Psychology, University of Texas at Austin
Austin, TX
Cyrus Eierud
Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Brad Caron
Department of Psychology, University of Texas at Austin
Austin, TX
Angela Laird
Department of Physics, Florida International University
Miami, FL
Paul Wighton
Martinos Center for Biomedical Imaging at MGH
Boston, MA
Dustin Moraczewski
Data Science and Sharing Team, National Institute of Mental Health
Bethesda, MD
Robert Oostenveld
Donders Centre For Cognitive Neuroimaging, Radboud University
Nijmegen, Netherlands
Arnaud Delorme
SCCN, INC, University of California San Diego
La Jolla, CA
Robert Smith
Florey Institute of Neuroscience and Mental Health
Melbourne, VIC
Oscar Esteban
Lausanne University Hospital and University of Lausanne
Lausanne, VD
Introduction:
Historically, neuroimaging data have been stored in a variety of unique file formats and directory structures, presenting obstacles in data sharing, scientific clarity, and rigor. The introduction of the Brain Imaging Data Structure (BIDS) [1] has been pivotal in addressing these issues by standardizing file system structures and metadata for raw neuroimaging data, leading to its widespread adoption [2]. Over time, BIDS has evolved beyond its original scope of MRI data, encompassing a broader range of imaging modalities, thanks to contributions from the community [3]. However, due to this evolution being mostly centered around raw data, BIDS currently lacks detailed descriptions for advanced data derivatives, particularly in brain connectivity research.
To address this gap, the BIDS connectivity project (https://pestillilab.github.io/bids-connectivity) is expanding the scope of BIDS derivatives. This extension includes both raw and minimally processed data, as well as more sophisticated derivatives from brain connectivity experiments. The project aims to establish standard descriptions for connectivity derivatives across six key data modalities: anatomical, diffusion-weighted, and functional MRI, along with PET, M/EEG, and iEEG. This initiative will significantly bolster research capabilities in terms of data generation, sharing, and replication of studies using published data derivatives. Additionally, It will streamline neuroimaging pipelines and processing, thereby accelerating research and development.
Methods:
The development of the BIDS Connectivity standard was community-driven, involving several stages: (1) A stakeholders' meeting held in September 2022, (2) Beginning drafts of new BIDS Extension Proposals (BEPs) with input from experts present at the meeting, (3) Feedback on these BEPs from the broader neuroimaging community was solicited in Spring 2023, (4) a workshop was conducted during OHBM 2023, (5) Community feedback obtained during this time period was incorporated into the BEPs, and (6) Integration of the Connectivity BEPs into BIDS.
Results:
Together, the BIDS Connectivity workshops in September 2022 and during OHBM 2023 saw the participation of over thirty expert investigators, who advanced five BEPs. These initial drafts, in line with BIDS BEP guidelines, represent the first comprehensive community-driven effort to standardize descriptions of brain connectivity data derivatives across six major neuroimaging modalities.
The five BEPs in development cover: (1) diffusion voxel-wise models, (2) diffusion tractography, (3) connectivity matrix schema, including seed-based connectivity methods, (4) dimensionality reduction-based networks, and (5) brain atlas specification. These BEPs have been open for feedback from the neuroimaging community since spring 2023.
Conclusions:
The establishment of a data-sharing standard for brain connectivity metrics is a crucial step toward enhancing best practices, scientific stringency, and transparency in the field of neuroimaging [4]. Following this process, the BEPs are expected to be merged into the main BIDS specification by summer 2024. Further meetings are scheduled for Spring 2024 to finalize community feedback integration and revise the BEPs.
This framework will facilitate the integration of results from various datasets and processing pipelines, boosting interoperability among diverse brain connectivity projects. This, in turn, will create opportunities for synergy across different levels of analysis of neuroimaging data, as network neuroscience can help combine data across modalities, spatial and temporal scales.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 1
Informatics Other
Keywords:
Data Organization
Open Data
Other - Data Standardization
1|2Indicates the priority used for review
Provide references using author date format
[1] Gorgolewski, K. J. (2016), The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data 3, 160044.
[2] Niso, G. (2022), Open and reproducible neuroimaging: From study inception to publication. NeuroImage 263, 119623.
[3] Poldrack, R. A. (2023), The past, present, and future of the brain imaging data structure (BIDS). arXiv preprint arXiv:2309.05768.
[4] Stall, S. (2019), Make scientific data FAIR. Nature 570, 27–29.